You can also use other AWS services like AWS Glue to load data from JSON to Redshift. Redshift also provides you with in-built SQL commands to carry out the data loading process. You can easily load data from JSON to Redshift via Amazon S3 or directly using third party Data Integration tools. JSON is popular because of its simplicity and ease of integration with most of the widely used applications. Applications use their format to generate data, out of which the most common data structure is JSON. It can be used for business reporting as well as analytics tools.In this digitized world, almost everything is generating data, and it has become a crucial task for a Data Engineer to store the data correctly to perform analytics. It can be used to process logs, perform ad-hoc analysis, and run interactive queries and joins. It can be used with Lambda to build serverless apps.Īthena can be used for complex queries on files, span multiple folders under S3 bucket. It is an S3 feature designed that works by retrieving a subset of an object’s data (using simple SQL expressions) instead of the entire object, which can be up to 5 terabytes in size. S3 Select can be used for simple queries based on a single S3 object. Of course, we used AWS Console for achieving that but you could use S3 Select or Athena using AWS SDK for your preferred language or AWS CLI. You get a total of 10 records aggregated from all files in the bucket as shown below:Īlso, you can now run other DML queries against this table not only select. Now you go back to Athena's query editor and choose our database athenademodb then you find the table added as tbl_query_data_s3_sql, the prefix plus the bucket's name, and finally, you can run SQL queries against your table as follow:.After crawling is done, you will see that 1 table is added.Now you can select the newly created crawler and run it. Finally, review all steps and hit "Finish".Create a database athenademodb and set the prefix added to tables to tbl_ and hit "Next".If the data in the bucket are frequently changing then you need to set the crawler to run periodically, in our case we go for Run on demand then "Next". Create an IAM role by adding a suffix for the Role name, in our case AthenaDemo.Configure "Data Store" step, by keeping the default selection so chosen data store as S3 and leave "Connection" field empty, and keep Specified path in my account, then you need fill in the path of the bucket, in our case s3://query-data-s3-sql then hit "Next" then no need to add another data sore, so another "Next". Being redirected to AWS Glue, now let's set up the crawler configuration, AthenaDemo as a name then specify "Crawler source type", in our case Data stores then we configure what exactly the repeat crawls of the data stores will crawl whether all folders or just new folders or changed folders, in our case we choose Crawl all folders.In order to create a table and retrieve the schema automatically we opt in "Create a crawler in AWS Glue" option, then hit "Create in AWS Glue".Go to "Data sources" from side menu, then "Connect data source", here you could choose where would you like to query your data from, in our case we're going to choose "S3 - AWS Glue Data Catalog".Navigate to "Athena" and first we need to set "Query result location" path in "Settings" to the same bucket in our case.using Athena and Glue against multiple files to be aggregated.Ĭopy the following JSON sample of 5 best football players in the world in 2022 according to Radiotimes magazine:.using S3 Select against one file (S3 object) or.In this blog, we are going to demo how to do that: But maybe there is a way you still can filter your data first in the cloud. You may have chosen to retrieve the entire object in your application and then filter out only the required data for further analysis. As a developer working on AWS cloud, you may encounter having an S3 object that contains JSON or CSV content that you want to run a SQL query against it.
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